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Computer Science > Artificial Intelligence

arXiv:2405.14411 (cs)
[Submitted on 23 May 2024 (v1), last revised 4 Sep 2024 (this version, v2)]

Title:Large Language Models for Explainable Decisions in Dynamic Digital Twins

Authors:Nan Zhang, Christian Vergara-Marcillo, Georgios Diamantopoulos, Jingran Shen, Nikos Tziritas, Rami Bahsoon, Georgios Theodoropoulos
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Abstract:Dynamic data-driven Digital Twins (DDTs) can enable informed decision-making and provide an optimisation platform for the underlying system. By leveraging principles of Dynamic Data-Driven Applications Systems (DDDAS), DDTs can formulate computational modalities for feedback loops, model updates and decision-making, including autonomous ones. However, understanding autonomous decision-making often requires technical and domain-specific knowledge. This paper explores using large language models (LLMs) to provide an explainability platform for DDTs, generating natural language explanations of the system's decision-making by leveraging domain-specific knowledge bases. A case study from smart agriculture is presented.
Comments: 9 pages, 3 figures, accepted by DDDAS2024 -- the 5th International Conference on Dynamic Data Driven Applications Systems
Subjects: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
Cite as: arXiv:2405.14411 [cs.AI]
  (or arXiv:2405.14411v2 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2405.14411

Submission history

From: Nan Zhang [view email]
[v1] Thu, 23 May 2024 10:32:38 UTC (1,654 KB)
[v2] Wed, 4 Sep 2024 06:00:56 UTC (1,654 KB)
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